Our project used data from Kaggle’s 2013 Yelp Challenge. This challenge included a subset of Yelp data from the metropolitan area of Phoenix, Arizona. Our data takes into account user reviews, ratings, and check-in data for a wide-range of businesses.
Data was acquired and transformed in the preprocessing.R file located within our repositories final-project folder. Our data source was provided as multiarray Json files, meaning each file is a collection of json data. We used stream_in function, which parses json data line-by-line from the data folder of our repository. The collections included three, large data for Yelp businesses, users, and reviews.
Once obtained, we prepared our data for our recommender system using the following transformations:
We choose to limit the scope to our recommender system to only businesses with tags related to food and beverages. There were originally 508 unique category tags listed within our business data. We manually filtered 112 targeted categories to subset our data.
We applied additional transformation to remove unnessacary data. There were 1,224 business in our data that were permanently closed. These companies accounted for 9.8% of all businesses, which were subsequently removed from our data. There were also 3 businesses in our dataset from outside of AZ that we also removed.
As a result of our transformations, our recommender data was shortened 4,828 unique businesses. This was further limited to 4,332 after randomly sampling our user-data. The output of which can be previewed below:
| business_id | categories | city | name | longitude | state | latitude |
|---|---|---|---|---|---|---|
| usAsSV36QmUej8–yvN-dg | Food, Grocery | Phoenix | Food City | -112.0854 | AZ | 33.39221 |
| PzOqRohWw7F7YEPBz6AubA | Food, Bagels, Delis, Restaurants | Glendale Az | Hot Bagels & Deli | -112.2003 | AZ | 33.71280 |
| qarobAbxGSHI7ygf1f7a_Q | Sandwiches, Restaurants | Gilbert | Jersey Mike’s Subs | -111.8120 | AZ | 33.37884 |
| JxVGJ9Nly2FFIs_WpJvkug | Pizza, Restaurants | Scottsdale | Sauce | -111.9263 | AZ | 33.61746 |
| Jj7bcQ6NDfKoz4TXwvYfMg | Burgers, Restaurants | Phoenix | Fuddruckers | -112.1162 | AZ | 33.56699 |
| JHp5mJvYe6UtM_QsklR-iw | Pizza, Restaurants | Scottsdale | Peter Piper Pizza | -111.9175 | AZ | 33.46613 |
We subset our review data from the subset of food and beverage businesses. This dropped our review data from 229,907 to 165,823 reviews. We later applied another filter to the data to only use reviews from 10,000 randomly sampled users. This further decreases reviews to 44,494 observations. Our review data can be previewed in two parts below:
| votes.funny | votes.useful | votes.cool | user_id | review_id | stars | date | business_id |
|---|---|---|---|---|---|---|---|
| 4 | 7 | 7 | wFweIWhv2fREZV_dYkz_1g | riFQ3vxNpP4rWLk_CSri2A | 5 | 2010-02-12 | zp713qNhx8d9KCJJnrw1xA |
| 2 | 4 | 3 | SBbftLzfYYKItOMFwOTIJg | HXP_0Ul-FCmA4f-k9CqvaQ | 3 | 2008-10-12 | supigcPNO9IKo6olaTNV-g |
| 1 | 4 | 2 | C6IOtaaYdLIT5fWd7ZYIuA | MuqugTuR5DdIPcZ2IVP3aQ | 3 | 2008-10-08 | 8FNO4D3eozpIjj0k3q5Zbg |
| 1 | 4 | 2 | RRTraCQw77EU4yZh0BBTag | B5h25WK28rJjx4KHm4gr7g | 4 | 2008-03-21 | wct7rZKyZqZftzmAU-vhWQ |
| 0 | 1 | 0 | kpbhy1zPewGDmdNfNqQp-g | hre97jjSwon4bn1muHKOJg | 4 | 2012-07-12 | i213sY5rhkfCO8cD-FPr1A |
| 0 | 1 | 0 | 8AMn6644NmBf96xGO3w6OA | S9OVpXat8k5YwWCn6FAgXg | 1 | 2012-05-04 | vvA3fbps4F9nGlAEYKk_sA |
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Drop what you’re doing and drive here. After I ate here I had to go back the next day for more. The food is that good. This cute little green building may have gone competely unoticed if I hadn’t been driving down Palm Rd to avoid construction. While waiting to turn onto 16th Street the “Grand Opening” sign caught my eye and my little yelping soul leaped for joy! A new place to try! It looked desolate from the outside but when I opened the door I was put at easy by the decor, smell and cleanliness inside. I ordered dinner for two, to go. The menu was awesome. I loved seeing all the variety: poblano peppers, mole, mahi mahi, mushrooms…something wrapped in banana leaves. It made it difficult to choose something. Here’s what I’ve had so far: La Condesa Shrimp Burro and Baja Sur Dogfish Shark Taco. They are both were very delicious meals but the shrimp burro stole the show. So much flavor. I snagged some bites from my hubbys mole and mahi mahi burros- mmmm such a delight. The salsa bar is endless. I really stocked up. I was excited to try the strawberry salsa but it was too hot, in fact it all was, but I’m a big wimp when it comes to hot peppers. The horchata is handmade and delicious. They throw pecans and some fruit in there too which is a yummy bonus! As if the good food wasn’t enough to win me over the art in this restaurant sho did! I’m a sucker for Mexican folk art and Frida Kahlo is my Oprah. There’s a painting of her and Diego hanging over the salsa bar, it’s amazing. All the paintings are great, love the artist. |
Last, we applied a similar filter to users to subset our data based on only our selected businesses. This decreased our user data from 43,873 to 35,268 distinct user_id observations. Do to processing constraints in R, we choose to randomly sample 10,000 users from these unique profiles.
The dataframe preview below shows aggregate user data for all reviews an individual user provided for yelp within our data selection.
| user_id | user_name | review_count | votes.funny | votes.useful | votes.cool | average_stars |
|---|---|---|---|---|---|---|
| –lMCM6K8-9NTvPlbCMXEA | Anne Marie | 1 | 0 | 0 | 0 | 4.0 |
| –LzFD0UDbYE-Oho3AhsOg | Shumai | 1 | 0 | 1 | 0 | 4.0 |
| –M-cIkGnH1KhnLaCOmoPQ | Emma | 1 | 2 | 2 | 2 | 5.0 |
| -01H9S7YxFrhRgNdvxmaVQ | Marc | 1 | 0 | 0 | 0 | 5.0 |
| -06LYbA4Qm_9E83KNT1Jrg | Brett | 2 | 0 | 0 | 0 | 4.5 |
| -0Ycl6yN0BsX1U70-SZOYw | Kate | 2 | 0 | 0 | 0 | 4.0 |
Next, we created our main dataframe by merging business and reviews on Business_ID. This dataframe will serve as the source of data for our recommender algorithms. The user and business unique keys were simplified from characters to numeric user/item identifiers.
This dataframe will be referenced later on when building our recommender matrices and algorithms. Review details were omitted in the preview for brevity.
| business_id | categories | city | name | longitude | state | latitude | votes.funny | votes.useful | votes.cool | user_id | review_id | stars | date | userID | itemID |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| usAsSV36QmUej8–yvN-dg | Food, Grocery | Phoenix | Food City | -112.0854 | AZ | 33.39221 | 0 | 0 | 0 | 1Eevry0X_8yb6yzsQilptg | F-R4pX3Ane7y3VlswhWrrQ | 3 | 2011-11-20 | 1 | 1 |
| PzOqRohWw7F7YEPBz6AubA | Food, Bagels, Delis, Restaurants | Glendale Az | Hot Bagels & Deli | -112.2003 | AZ | 33.71280 | 0 | 1 | 0 | Iycf9KNRhxvR187Qu2zZHg | hg7rapz_KzAqhoOFYhXVoQ | 4 | 2012-06-11 | 2 | 2 |
| qarobAbxGSHI7ygf1f7a_Q | Sandwiches, Restaurants | Gilbert | Jersey Mike’s Subs | -111.8120 | AZ | 33.37884 | 1 | 0 | 0 | 4UypETvlv8cl0jKFxHh3Zw | OhWvwGTbiuT4tnLpK-iC-w | 2 | 2012-08-27 | 3 | 3 |
| qarobAbxGSHI7ygf1f7a_Q | Sandwiches, Restaurants | Gilbert | Jersey Mike’s Subs | -111.8120 | AZ | 33.37884 | 0 | 1 | 0 | 5j7qmDZTAetaH0yXFnAFyw | rTghOy2OZxdmI6ofRzI0Bg | 3 | 2012-03-09 | 4 | 3 |
| qarobAbxGSHI7ygf1f7a_Q | Sandwiches, Restaurants | Gilbert | Jersey Mike’s Subs | -111.8120 | AZ | 33.37884 | 1 | 2 | 1 | uNbB1uR4EBhmygUc3IfPAw | EY-eYBoXIjn2k2X_ZDTpJA | 4 | 2012-05-10 | 5 | 3 |
| JxVGJ9Nly2FFIs_WpJvkug | Pizza, Restaurants | Scottsdale | Sauce | -111.9263 | AZ | 33.61746 | 0 | 0 | 0 | l_6XDatGLHfkGxl8BjI2Ag | imbU3ZZlDf5SIKHkaEskaw | 5 | 2011-09-22 | 6 | 4 |
Add data visualizations.
We tested 3 recommender algorithms to see which had the best performance metrics for our recommender system. To test the algorithsm, we first had to create a user-item matrix and then split our data into training and test sets.
Matrix Building
We converted our raw ratings data into a user-item matrix to test and train our subsequent recommender system algorithms. The matrix was saved as a realRatingMatrix for processing purposes later on using the recommenderlab package.
The matrix data can be viewed below.
# spread data from long to wide format
matrix_data <- df %>% select(userID, itemID, stars) %>% spread(itemID, stars)
# set row names to userid
rownames(matrix_data) <- matrix_data$userID
# remove userid from columns
matrix_data <- matrix_data %>% select(-userID)
# convert to matrix
ui_mat <- matrix_data %>% as.matrix()
# store matrix as realRatingMatrix
ui_mat <- as(ui_mat, "realRatingMatrix")
# view matrix data
matrix_dataTrain and Test Splits
Our data was split into training and tests sets for model evaluation of both two recommender algorithms. We split our data with 10 k-folds using the recommenderlab package. 80% of data was retained for training and 20% for testing purposes.
# evaluation method with 80% of data for train and 20% for test
set.seed(1000)
evalu <- evaluationScheme(ui_mat, method = "split", train = 0.8, given = 1,
goodRating = 1, k = 10)
# Prep data
train <- getData(evalu, "train") # Training Dataset
dev_test <- getData(evalu, "known") # Test data from evaluationScheme of type KNOWN
test <- getData(evalu, "unknown") # Unknow datset used for RMSE / model evaluationNow that we have the user and Business Rating adjsusted where 0 indicates No Feedback, -1 Indicates Negative Feedback and 1 indicates postive feedback.
I decided to use Jaccard Distance to measure the similarity between Busienss profiles,
Algo
Set up:
# install.packages('devtools') devtools::install_github('rstudio/sparklyr')
# spark_install(version = '2.1.0')
library(sparklyr)
# configure/initiate connection
config <- spark_config()
config$spark.executor.memory <- "8G"
config$spark.executor.cores <- 2
config$spark.executor.instances <- 3
config$spark.dynamicAllocation.enabled <- "false"
sc <- spark_connect(master = "local", config = config, version = "2.1.0")
spark_version(sc)FALSE [1] '2.1.0'# prepare data
spark_data <- df %>% select(user_id, itemID, name, stars) %>% mutate(item = paste(itemID,
"-", name)) %>% select(user_id, item, stars)
user_item_ratings <- sdf_copy_to(sc, spark_data, "user_item_ratings", overwrite = TRUE)
user_item_ratings <- user_item_ratings %>% ft_string_indexer(input_col = "user_id",
output_col = "user_index") %>% ft_string_indexer(input_col = "item", output_col = "item_index")
partition <- sdf_random_split(user_item_ratings, training = 0.8, testing = 0.2)
spark_test <- sdf_register(partition$testing, "spark_test")
spark_train <- sdf_register(partition$training, "spark_train")
tidy_train <- tbl(sc, "spark_train") %>% select(user_index, item_index, stars)ALS predictions
sparkALS <- ml_als(tidy_train, max_iter = 5, nonnegative = TRUE, rating_col = "stars",
user_col = "user_index", item_col = "item_index")
spark_test <- tbl(sc, "spark_test")
predictions <- ml_predict(sparkALS, spark_test)
prediction <- collect(predictions)
# Remove NaN due to data set splitting
prediction <- prediction[!is.na(prediction$prediction), ]
# Metrics
mseSpark <- mean((prediction$stars - prediction$prediction)^2)
rmseSpark <- sqrt(mseSpark)
maeSpark <- mean(abs(prediction$stars - prediction$prediction))| user_id | item | stars | prediction |
|---|---|---|---|
| fczQCSmaWF78toLEmb0Zsw | 173 - Lalibela Ethiopian Cafe | 4 | 3.6 |
| fczQCSmaWF78toLEmb0Zsw | 2696 - Sakana Sushi & Grill | 4 | 4.1 |
| fczQCSmaWF78toLEmb0Zsw | 3203 - Renegade Tap & Kitchen | 3 | 4.6 |
| fczQCSmaWF78toLEmb0Zsw | 2120 - Sala Thai | 4 | 3.5 |
| fczQCSmaWF78toLEmb0Zsw | 3951 - Benihana | 3 | 3.1 |
| fczQCSmaWF78toLEmb0Zsw | 3059 - Julia Baker Confections | 5 | 4.8 |
Evaluate performance metrics.
FALSE [,1]
FALSE mseSpark 1.924279
FALSE rmseSpark 1.387184
FALSE maeSpark 1.091038FALSE NULLCompare algorithms performance. Select most effective to build recommender system.
Test system
Final conlusion. Explain limitations of system. Make recommendations for future improvements.